Overview

Dataset statistics

Number of variables13
Number of observations725607
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.0 MiB
Average record size in memory104.0 B

Variable types

DateTime1
Numeric12

Alerts

enhanced_speed is highly correlated with heart_rate and 1 other fieldsHigh correlation
cadence is highly correlated with heart_rate and 1 other fieldsHigh correlation
slope_steep is highly correlated with slope_ascentHigh correlation
slope_ascent is highly correlated with slope_steepHigh correlation
slope_descent is highly correlated with slope_steepHigh correlation
heart_rate is highly correlated with enhanced_speed and 1 other fieldsHigh correlation
enhanced_altitude is highly correlated with temp and 1 other fieldsHigh correlation
temp is highly correlated with enhanced_altitude and 2 other fieldsHigh correlation
wind_speed is highly correlated with enhanced_altitude and 2 other fieldsHigh correlation
wind_direct is highly correlated with temp and 1 other fieldsHigh correlation
timestamp has unique values Unique
wind_direct has 7686 (1.1%) zeros Zeros
rain has 410395 (56.6%) zeros Zeros
slope_steep has 398827 (55.0%) zeros Zeros
slope_ascent has 557781 (76.9%) zeros Zeros
slope_descent has 566118 (78.0%) zeros Zeros

Reproduction

Analysis started2022-11-02 20:39:06.138677
Analysis finished2022-11-02 20:39:57.948301
Duration51.81 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

timestamp
Date

UNIQUE

Distinct725607
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
Minimum2021-11-22 08:05:16
Maximum2022-11-01 07:29:54
2022-11-02T21:39:58.060217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:58.224930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

heart_rate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct137
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.7379008
Minimum58
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2022-11-02T21:39:58.432909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile119
Q1132
median140
Q3154
95-th percentile179
Maximum196
Range138
Interquartile range (IQR)22

Descriptive statistics

Standard deviation18.15999403
Coefficient of variation (CV)0.1263410271
Kurtosis-0.06503235764
Mean143.7379008
Median Absolute Deviation (MAD)10
Skewness0.4417108439
Sum104297227
Variance329.7853831
MonotonicityNot monotonic
2022-11-02T21:39:58.616472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13723806
 
3.3%
13622683
 
3.1%
13422310
 
3.1%
13922235
 
3.1%
14022110
 
3.0%
13821787
 
3.0%
13520882
 
2.9%
13320797
 
2.9%
13120071
 
2.8%
13219540
 
2.7%
Other values (127)509386
70.2%
ValueCountFrequency (%)
581
 
< 0.1%
602
 
< 0.1%
623
 
< 0.1%
635
< 0.1%
642
 
< 0.1%
655
< 0.1%
663
 
< 0.1%
676
< 0.1%
688
< 0.1%
696
< 0.1%
ValueCountFrequency (%)
1961
 
< 0.1%
1953
 
< 0.1%
19411
 
< 0.1%
19323
 
< 0.1%
19275
 
< 0.1%
191127
 
< 0.1%
190487
 
0.1%
189992
0.1%
1881423
0.2%
1871138
0.2%

enhanced_speed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4592
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.99702927
Minimum3.006
Maximum37.3536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2022-11-02T21:39:58.817390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.006
5-th percentile7.6932
Q110.548
median11.4876
Q312.7296
95-th percentile17.9388
Maximum37.3536
Range34.3476
Interquartile range (IQR)2.1816

Descriptive statistics

Standard deviation3.068264164
Coefficient of variation (CV)0.2557519945
Kurtosis1.676491196
Mean11.99702927
Median Absolute Deviation (MAD)1.0404
Skewness0.5875225596
Sum8705128.417
Variance9.41424498
MonotonicityNot monotonic
2022-11-02T21:39:58.993212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.5567522
 
1.0%
11.45527184
 
1.0%
11.25367127
 
1.0%
11.15287067
 
1.0%
11.35447033
 
1.0%
11.65686619
 
0.9%
11.0526490
 
0.9%
11.38686442
 
0.9%
11.75766370
 
0.9%
11.2866329
 
0.9%
Other values (4582)657424
90.6%
ValueCountFrequency (%)
3.0064
 
< 0.1%
3.01321
 
< 0.1%
3.02495
< 0.1%
3.02764
 
< 0.1%
3.03121
 
< 0.1%
3.03484
 
< 0.1%
3.056472
< 0.1%
3.063
 
< 0.1%
3.06722
 
< 0.1%
3.07084
 
< 0.1%
ValueCountFrequency (%)
37.35361
< 0.1%
33.59161
< 0.1%
30.90241
< 0.1%
30.83761
< 0.1%
30.61
< 0.1%
30.23281
< 0.1%
30.19682
< 0.1%
30.16441
< 0.1%
30.1321
< 0.1%
30.03121
< 0.1%

distance
Real number (ℝ≥0)

Distinct584854
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7071.774407
Minimum0
Maximum26408.58
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2022-11-02T21:39:59.139326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile530.226
Q12844.295
median6209.17
Q310304.765
95-th percentile16884.062
Maximum26408.58
Range26408.58
Interquartile range (IQR)7460.47

Descriptive statistics

Standard deviation5107.410842
Coefficient of variation (CV)0.7222247979
Kurtosis-0.127225818
Mean7071.774407
Median Absolute Deviation (MAD)3651.7
Skewness0.7192083419
Sum5131329012
Variance26085645.51
MonotonicityNot monotonic
2022-11-02T21:39:59.276777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3616.467
 
< 0.1%
4520.746
 
< 0.1%
9411.26
 
< 0.1%
727.626
 
< 0.1%
2377.576
 
< 0.1%
1556.586
 
< 0.1%
8736.226
 
< 0.1%
6809.786
 
< 0.1%
5957.816
 
< 0.1%
1472.76
 
< 0.1%
Other values (584844)725546
> 99.9%
ValueCountFrequency (%)
02
< 0.1%
0.491
< 0.1%
0.511
< 0.1%
0.671
< 0.1%
0.871
< 0.1%
0.941
< 0.1%
1.082
< 0.1%
1.151
< 0.1%
1.191
< 0.1%
1.251
< 0.1%
ValueCountFrequency (%)
26408.581
< 0.1%
26402.761
< 0.1%
26389.241
< 0.1%
26371.511
< 0.1%
26363.731
< 0.1%
26361.951
< 0.1%
26360.592
< 0.1%
26353.621
< 0.1%
26348.241
< 0.1%
26347.241
< 0.1%

enhanced_altitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8222
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.9288083
Minimum129.4
Maximum2438.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2022-11-02T21:39:59.407967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum129.4
5-th percentile187.8
Q1198.2
median247.6
Q3384.2
95-th percentile659.8
Maximum2438.2
Range2308.8
Interquartile range (IQR)186

Descriptive statistics

Standard deviation231.2587409
Coefficient of variation (CV)0.6967118704
Kurtosis16.78684266
Mean331.9288083
Median Absolute Deviation (MAD)54.6
Skewness3.625091051
Sum240849866.8
Variance53480.60522
MonotonicityNot monotonic
2022-11-02T21:39:59.543100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
376.616900
 
2.3%
197.64888
 
0.7%
197.24497
 
0.6%
1984434
 
0.6%
197.84429
 
0.6%
197.44345
 
0.6%
198.64240
 
0.6%
196.84173
 
0.6%
1993868
 
0.5%
198.23853
 
0.5%
Other values (8212)669980
92.3%
ValueCountFrequency (%)
129.45
 
< 0.1%
129.61
 
< 0.1%
129.89
 
< 0.1%
13026
< 0.1%
130.220
 
< 0.1%
130.414
 
< 0.1%
130.614
 
< 0.1%
130.814
 
< 0.1%
13151
< 0.1%
131.226
< 0.1%
ValueCountFrequency (%)
2438.21
< 0.1%
24381
< 0.1%
2437.41
< 0.1%
2433.41
< 0.1%
2432.81
< 0.1%
24311
< 0.1%
2430.61
< 0.1%
2429.81
< 0.1%
2429.21
< 0.1%
2428.21
< 0.1%

cadence
Real number (ℝ≥0)

HIGH CORRELATION

Distinct98
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.88773399
Minimum31
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2022-11-02T21:39:59.689073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile81
Q185
median86
Q388
95-th percentile93
Maximum128
Range97
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.715580412
Coefficient of variation (CV)0.07819021529
Kurtosis16.66057908
Mean85.88773399
Median Absolute Deviation (MAD)2
Skewness-3.377655668
Sum62320741
Variance45.09902027
MonotonicityNot monotonic
2022-11-02T21:40:00.020044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86129296
17.8%
87116370
16.0%
85100010
13.8%
8465768
9.1%
8865666
9.0%
9136057
 
5.0%
8933733
 
4.6%
8333221
 
4.6%
9030295
 
4.2%
9223391
 
3.2%
Other values (88)91800
12.7%
ValueCountFrequency (%)
314
 
< 0.1%
3211
 
< 0.1%
3311
 
< 0.1%
346
 
< 0.1%
3515
 
< 0.1%
3616
 
< 0.1%
3724
 
< 0.1%
3832
< 0.1%
3949
< 0.1%
4070
< 0.1%
ValueCountFrequency (%)
1282
 
< 0.1%
12711
 
< 0.1%
1266
 
< 0.1%
1258
 
< 0.1%
12414
 
< 0.1%
12317
 
< 0.1%
12215
 
< 0.1%
12136
< 0.1%
12054
< 0.1%
11947
< 0.1%

temp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct179
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.86533137
Minimum0
Maximum29.7
Zeros1258
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2022-11-02T21:40:00.168937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8
Q110
median16.2
Q319.6
95-th percentile24.7
Maximum29.7
Range29.7
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation6.988202607
Coefficient of variation (CV)0.4701006949
Kurtosis-0.6668890787
Mean14.86533137
Median Absolute Deviation (MAD)4.5
Skewness-0.4344355679
Sum10786388.5
Variance48.83497568
MonotonicityNot monotonic
2022-11-02T21:40:00.285849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.222629
 
3.1%
18.216028
 
2.2%
18.515081
 
2.1%
13.613445
 
1.9%
17.212703
 
1.8%
16.212097
 
1.7%
14.511844
 
1.6%
22.811135
 
1.5%
24.210359
 
1.4%
21.210126
 
1.4%
Other values (169)590160
81.3%
ValueCountFrequency (%)
01258
 
0.2%
0.1169
 
< 0.1%
0.25122
0.7%
0.33989
0.5%
0.42599
0.4%
0.52805
0.4%
0.63242
0.4%
0.81354
 
0.2%
0.94806
0.7%
1.42585
0.4%
ValueCountFrequency (%)
29.71325
 
0.2%
27.45364
0.7%
27.25253
0.7%
26.74507
0.6%
26.55504
0.8%
26.23735
0.5%
25.23284
 
0.5%
24.78212
1.1%
24.64981
0.7%
24.37246
1.0%

wind_speed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct106
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.251455264
Minimum0
Maximum28.1
Zeros6855
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2022-11-02T21:40:00.418149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.6
Q15.7
median7
Q39.7
95-th percentile16.2
Maximum28.1
Range28.1
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.913364814
Coefficient of variation (CV)0.4742635921
Kurtosis4.348961023
Mean8.251455264
Median Absolute Deviation (MAD)1.8
Skewness1.741321065
Sum5987313.7
Variance15.31442417
MonotonicityNot monotonic
2022-11-02T21:40:00.533231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.835463
 
4.9%
5.828221
 
3.9%
6.524876
 
3.4%
9.722516
 
3.1%
5.217898
 
2.5%
6.617775
 
2.4%
5.117711
 
2.4%
6.217183
 
2.4%
7.316966
 
2.3%
916883
 
2.3%
Other values (96)510115
70.3%
ValueCountFrequency (%)
06855
0.9%
3.23244
 
0.4%
3.81955
 
0.3%
43938
0.5%
4.22196
 
0.3%
4.37189
1.0%
4.4786
 
0.1%
4.59284
1.3%
4.61708
 
0.2%
4.78436
1.2%
ValueCountFrequency (%)
28.11608
 
0.2%
27.81273
 
0.2%
25.61032
 
0.1%
25.51276
 
0.2%
24.81956
 
0.3%
21.81083
 
0.1%
21.11125
 
0.2%
19.75126
0.7%
19.41060
 
0.1%
18.43707
0.5%

wind_direct
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct182
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.187902
Minimum0
Maximum358
Zeros7686
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2022-11-02T21:40:00.655214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q1123
median235
Q3294
95-th percentile343
Maximum358
Range358
Interquartile range (IQR)171

Descriptive statistics

Standard deviation103.0447797
Coefficient of variation (CV)0.4973494046
Kurtosis-0.9519302321
Mean207.187902
Median Absolute Deviation (MAD)74
Skewness-0.4834566878
Sum150336992
Variance10618.22662
MonotonicityNot monotonic
2022-11-02T21:40:00.776880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24615152
 
2.1%
29815015
 
2.1%
8313745
 
1.9%
34313187
 
1.8%
30712655
 
1.7%
31712012
 
1.7%
27111734
 
1.6%
21011616
 
1.6%
32810763
 
1.5%
13110568
 
1.5%
Other values (172)599160
82.6%
ValueCountFrequency (%)
07686
1.1%
12862
 
0.4%
35112
0.7%
51204
 
0.2%
74981
0.7%
95223
0.7%
104719
0.7%
122268
 
0.3%
131282
 
0.2%
175242
0.7%
ValueCountFrequency (%)
3586881
0.9%
3571571
 
0.2%
3554271
 
0.6%
3541644
 
0.2%
3531663
 
0.2%
3524404
 
0.6%
3511864
 
0.3%
3472650
 
0.4%
3445722
0.8%
34313187
1.8%

rain
Real number (ℝ≥0)

ZEROS

Distinct69
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.627155058
Minimum0
Maximum84.6
Zeros410395
Zeros (%)56.6%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2022-11-02T21:40:00.905893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile13.2
Maximum84.6
Range84.6
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation8.284247731
Coefficient of variation (CV)3.153315107
Kurtosis66.90221515
Mean2.627155058
Median Absolute Deviation (MAD)0
Skewness7.381606365
Sum1906282.1
Variance68.62876047
MonotonicityNot monotonic
2022-11-02T21:40:01.022697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0410395
56.6%
0.522338
 
3.1%
0.119937
 
2.7%
7.613376
 
1.8%
3.312859
 
1.8%
0.312612
 
1.7%
1.111557
 
1.6%
0.211136
 
1.5%
1.410561
 
1.5%
2.88796
 
1.2%
Other values (59)192040
26.5%
ValueCountFrequency (%)
0410395
56.6%
0.119937
 
2.7%
0.211136
 
1.5%
0.312612
 
1.7%
0.48714
 
1.2%
0.522338
 
3.1%
0.61505
 
0.2%
0.71311
 
0.2%
0.86548
 
0.9%
0.91537
 
0.2%
ValueCountFrequency (%)
84.65223
0.7%
23.31337
 
0.2%
22.64519
0.6%
21.96401
0.9%
19.35419
0.7%
18.93365
0.5%
18.53198
0.4%
181523
 
0.2%
16.11882
 
0.3%
16957
 
0.1%

slope_steep
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct65517
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.317642683
Minimum0
Maximum45
Zeros398827
Zeros (%)55.0%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2022-11-02T21:40:01.149956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.899705015
95-th percentile13.21585903
Maximum45
Range45
Interquartile range (IQR)5.899705015

Descriptive statistics

Standard deviation5.724116275
Coefficient of variation (CV)1.725356472
Kurtosis16.43551932
Mean3.317642683
Median Absolute Deviation (MAD)0
Skewness3.365246875
Sum2407304.754
Variance32.76550713
MonotonicityNot monotonic
2022-11-02T21:40:01.281440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0398827
55.0%
452503
 
0.3%
6.153846154968
 
0.1%
6.666666667901
 
0.1%
6.309148265703
 
0.1%
6.472491909680
 
0.1%
6.329113924672
 
0.1%
6.493506494641
 
0.1%
6.230529595620
 
0.1%
6.006006006583
 
0.1%
Other values (65507)318509
43.9%
ValueCountFrequency (%)
0398827
55.0%
0.48661800491
 
< 0.1%
0.60132291041
 
< 0.1%
0.60404711571
 
< 0.1%
0.6410256411
 
< 0.1%
0.67159167231
 
< 0.1%
0.67476383271
 
< 0.1%
0.67521944631
 
< 0.1%
0.7057163021
 
< 0.1%
0.72939460251
 
< 0.1%
ValueCountFrequency (%)
452503
0.3%
451
 
< 0.1%
44.989775051
 
< 0.1%
44.943820222
 
< 0.1%
44.943820222
 
< 0.1%
44.943820223
 
< 0.1%
44.943820221
 
< 0.1%
44.943820222
 
< 0.1%
44.943820221
 
< 0.1%
44.943820222
 
< 0.1%

slope_ascent
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct70
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07660538005
Minimum0
Maximum10
Zeros557781
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2022-11-02T21:40:01.428680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1938750442
Coefficient of variation (CV)2.530828045
Kurtosis145.4521861
Mean0.07660538005
Median Absolute Deviation (MAD)0
Skewness6.600243448
Sum55585.4
Variance0.03758753278
MonotonicityNot monotonic
2022-11-02T21:40:01.556416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0557781
76.9%
0.270644
 
9.7%
0.240130
 
5.5%
0.419704
 
2.7%
0.47911
 
1.1%
0.67855
 
1.1%
13367
 
0.5%
0.83293
 
0.5%
0.22938
 
0.4%
0.62863
 
0.4%
Other values (60)9121
 
1.3%
ValueCountFrequency (%)
0557781
76.9%
0.22938
 
0.4%
0.240130
 
5.5%
0.270644
 
9.7%
0.2181
 
< 0.1%
0.484
 
< 0.1%
0.41740
 
0.2%
0.419704
 
2.7%
0.47911
 
1.1%
0.62863
 
0.4%
ValueCountFrequency (%)
109
< 0.1%
9.21
 
< 0.1%
8.81
 
< 0.1%
8.41
 
< 0.1%
7.61
 
< 0.1%
7.41
 
< 0.1%
6.82
 
< 0.1%
6.21
 
< 0.1%
5.61
 
< 0.1%
5.41
 
< 0.1%

slope_descent
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct70
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07482066739
Minimum0
Maximum10
Zeros566118
Zeros (%)78.0%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2022-11-02T21:40:01.690360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1931662291
Coefficient of variation (CV)2.581722883
Kurtosis77.02879606
Mean0.07482066739
Median Absolute Deviation (MAD)0
Skewness5.446922976
Sum54290.4
Variance0.03731319206
MonotonicityNot monotonic
2022-11-02T21:40:01.813854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0566118
78.0%
0.265047
 
9.0%
0.238451
 
5.3%
0.419006
 
2.6%
0.47704
 
1.1%
0.67657
 
1.1%
13478
 
0.5%
0.83367
 
0.5%
0.63203
 
0.4%
0.22086
 
0.3%
Other values (60)9490
 
1.3%
ValueCountFrequency (%)
0566118
78.0%
0.22086
 
0.3%
0.238451
 
5.3%
0.265047
 
9.0%
0.299
 
< 0.1%
0.466
 
< 0.1%
0.41656
 
0.2%
0.419006
 
2.6%
0.47704
 
1.1%
0.63203
 
0.4%
ValueCountFrequency (%)
102
< 0.1%
8.42
< 0.1%
7.81
< 0.1%
7.61
< 0.1%
6.81
< 0.1%
6.61
< 0.1%
6.41
< 0.1%
6.41
< 0.1%
6.21
< 0.1%
61
< 0.1%

Interactions

2022-11-02T21:39:53.096874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:21.976875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:24.976618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:28.337801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:31.086230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:33.705100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:37.110299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:39.651617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:42.144612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:44.782574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:47.286045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:50.216837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:53.338020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:22.201931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:25.287909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:28.576393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:31.320135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:33.925531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:37.397181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:39.862587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:42.356290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:44.994729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:47.495946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:50.449766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:53.638093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:22.350461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:25.558426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:28.839046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:31.543568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:34.317864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:37.617472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:40.072269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:42.566568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:45.200400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:47.713033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:50.679170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:53.875462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:22.492324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:25.849815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:29.118698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:31.768764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:34.535088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:37.818269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:40.279626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:42.781360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:45.402677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:47.935876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:50.913188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:54.100354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:22.631400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:26.170994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:29.329071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:31.981996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:34.748643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:38.016170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:40.480620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:42.993723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:45.602523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:48.176189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T21:39:54.318870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:22.773444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:26.522639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:29.541853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:32.187179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:35.002164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:38.215269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:40.677730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:43.364326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:45.797164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:48.437964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:51.382634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:54.548970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:22.956944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T21:39:29.750585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:32.396715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:35.267257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:38.423806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:40.887459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:43.556500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:45.997957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:48.696554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T21:39:35.595021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:38.622218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:41.096038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:43.755174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T21:39:48.997514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:51.826477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:55.047291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:23.407828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:27.394698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:30.196288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:32.841226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:35.928818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:38.828901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:41.319913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:43.961524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:46.462580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:49.246968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:52.220444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:55.261055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:23.743280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:27.626743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:30.422300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:33.045994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:36.207182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:39.028992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:41.519175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:44.165431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:46.658213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:49.511617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:52.408245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:55.479564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:24.127460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:27.885841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:30.641481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:33.260137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:36.514882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:39.225208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:41.721565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:44.368806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:46.863919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:49.757782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:52.610772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:55.717633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:24.632261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:28.116853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:30.855157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:33.479099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:36.847816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:39.439104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:41.939083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:44.575639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:47.069569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:49.987496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T21:39:52.869298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-02T21:40:01.922702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-02T21:40:02.067207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T21:40:02.231887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T21:40:02.386323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T21:40:02.538730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T21:39:55.953022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-02T21:39:56.687656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

timestampheart_rateenhanced_speeddistanceenhanced_altitudecadencetempwind_speedwind_directrainslope_steepslope_ascentslope_descent
02021-11-22 08:05:1685.06.51602.00249.456.05.38.7320.00.50.0000000.00.0
12021-11-22 08:05:2287.011.152823.28249.486.05.38.7320.00.50.0000000.00.0
22021-11-22 08:05:2690.014.644839.91249.486.05.38.7320.00.50.0000000.00.0
32021-11-22 08:05:2993.014.108452.04249.086.05.38.7320.00.53.2976090.00.4
42021-11-22 08:05:3096.013.132856.29249.285.05.38.7320.00.54.7058820.20.0
52021-11-22 08:05:3299.011.858463.36248.683.05.38.7320.00.58.4865630.00.6
62021-11-22 08:05:34101.011.556067.97248.884.05.38.7320.00.54.3383950.20.0
72021-11-22 08:05:35102.011.052069.32249.284.05.38.7320.00.529.6296300.40.0
82021-11-22 08:05:37105.07.020071.68249.283.05.38.7320.00.50.0000000.00.0
92021-11-22 08:05:41108.07.524080.99249.280.05.38.7320.00.50.0000000.00.0

Last rows

timestampheart_rateenhanced_speeddistanceenhanced_altitudecadencetempwind_speedwind_directrainslope_steepslope_ascentslope_descent
7255972022-11-01 07:29:45158.015.048011163.65244.092.012.64.8307.01.49.5923260.40.0
7255982022-11-01 07:29:46158.014.979611167.81244.292.012.64.8307.01.44.8076920.20.0
7255992022-11-01 07:29:47159.014.914811171.96244.491.012.64.8307.01.44.8192770.20.0
7256002022-11-01 07:29:48158.014.781611176.06244.690.012.64.8307.01.44.8780490.20.0
7256012022-11-01 07:29:49159.014.644811180.14245.090.012.64.8307.01.49.8039220.40.0
7256022022-11-01 07:29:50161.014.580011184.18245.290.012.64.8307.01.44.9504950.20.0
7256032022-11-01 07:29:51160.014.544011188.23245.490.012.64.8307.01.44.9382720.20.0
7256042022-11-01 07:29:52161.014.612411192.28245.491.012.64.8307.01.40.0000000.00.0
7256052022-11-01 07:29:53162.014.644811196.35245.890.012.64.8307.01.49.8280100.40.0
7256062022-11-01 07:29:54161.014.378411200.34246.089.012.64.8307.01.45.0125310.20.0